"""simple docstring"""







import tensorflow as tf

from ...tf_utils import shape_list







class 			UpperCamelCase						(     tf.keras.layers.Layer    ):
   def __init__(     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase=1	,__UpperCamelCase=False	,**__UpperCamelCase     )      -> Optional[int]:


        '''simple docstring'''


        super().__init__(**_A     )

        lowercase_      :							int						=      vocab_size
        lowercase_      :							Any						=      d_embed
        lowercase_      :							Dict						=      d_proj

        lowercase_      :							str						=      cutoffs + [vocab_size]
        lowercase_      :							Optional[Any]						=      [0] + self.cutoffs
        lowercase_      :							Tuple						=      div_val

        lowercase_      :							Dict						=      self.cutoffs[0]
        lowercase_      :							str						=      len(self.cutoffs     ) - 1
        lowercase_      :							Any						=      self.shortlist_size + self.n_clusters
        lowercase_      :							Tuple						=      keep_order

        lowercase_      :							str						=      []
        lowercase_      :							Any						=      []
   def      _UpperCAmelCase  (     self	,__UpperCamelCase     )      -> Any:


        '''simple docstring'''


        if self.n_clusters > 0:
             lowercase_      :							Optional[Any]						=      self.add_weight(
                 shape=(self.n_clusters, self.d_embed)	,initializer='zeros'	,trainable=_A	,name='cluster_weight'     )
             lowercase_      :							Tuple						=      self.add_weight(
                 shape=(self.n_clusters,)	,initializer='zeros'	,trainable=_A	,name='cluster_bias'     )

        if self.div_val == 1:
             for i in range(len(self.cutoffs     )     ):
                  if self.d_proj != self.d_embed:
                       lowercase_      :							Optional[int]						=      self.add_weight(
                           shape=(self.d_embed, self.d_proj)	,initializer='zeros'	,trainable=_A	,name=f'''out_projs_._{i}'''	,)
                       self.out_projs.append(_A     )
                  else:
                       self.out_projs.append(_A     )
                  lowercase_      :							str						=      self.add_weight(
                      shape=(self.vocab_size, self.d_embed)	,initializer='zeros'	,trainable=_A	,name=f'''out_layers_._{i}_._weight'''	,)
                  lowercase_      :							str						=      self.add_weight(
                      shape=(self.vocab_size,)	,initializer='zeros'	,trainable=_A	,name=f'''out_layers_._{i}_._bias'''	,)
                  self.out_layers.append((weight, bias)     )
        else:
             for i in range(len(self.cutoffs     )     ):
                  lowercase_      :							List[Any]						=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
                  lowercase_      :							Optional[int]						=      self.d_embed // (self.div_val**i)

                  lowercase_      :							Optional[int]						=      self.add_weight(
                      shape=(d_emb_i, self.d_proj)	,initializer='zeros'	,trainable=_A	,name=f'''out_projs_._{i}'''     )
                  self.out_projs.append(_A     )
                  lowercase_      :							Optional[int]						=      self.add_weight(
                      shape=(r_idx - l_idx, d_emb_i)	,initializer='zeros'	,trainable=_A	,name=f'''out_layers_._{i}_._weight'''	,)
                  lowercase_      :							Optional[int]						=      self.add_weight(
                      shape=(r_idx - l_idx,)	,initializer='zeros'	,trainable=_A	,name=f'''out_layers_._{i}_._bias'''	,)
                  self.out_layers.append((weight, bias)     )
        super().build(_A     )
   @staticmethod
   def      _UpperCAmelCase  (     __UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase=None     )      -> Dict:


        '''simple docstring'''


        lowercase_      :							Tuple						=      x
        if proj is not None:
             lowercase_      :							int						=      tf.einsum('ibd,ed->ibe'	,_A	,_A     )
        return tf.einsum('ibd,nd->ibn'	,_A	,_A     ) + b
   @staticmethod
   def      _UpperCAmelCase  (     __UpperCamelCase	,__UpperCamelCase     )      -> int:


        '''simple docstring'''


        lowercase_      :							Optional[Any]						=      shape_list(_A     )
        lowercase_      :							Any						=      tf.range(lp_size[0]	,dtype=target.dtype     )
        lowercase_      :							Optional[int]						=      tf.stack([r, target]	,1     )
        return tf.gather_nd(_A	,_A     )







   def      _UpperCAmelCase  (     self	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase=True	,__UpperCamelCase=False     )      -> int:


        '''simple docstring'''


        lowercase_      :							Optional[int]						=      0
        if self.n_clusters == 0:
             lowercase_      :							Dict						=      self._logit(_A	,self.out_layers[0][0]	,self.out_layers[0][1]	,self.out_projs[0]     )
             if target is not None:
                  lowercase_      :							List[str]						=      tf.nn.sparse_softmax_cross_entropy_with_logits(labels=_A	,logits=_A     )
             lowercase_      :							List[Any]						=      tf.nn.log_softmax(_A	,axis=-1     )
        else:
             lowercase_      :							Optional[Any]						=      shape_list(_A     )
             lowercase_      :							Optional[int]						=      []
             lowercase_      :							List[Any]						=      tf.zeros(hidden_sizes[:2]     )
             for i in range(len(self.cutoffs     )     ):
                  lowercase_      :							int						=      self.cutoff_ends[i], self.cutoff_ends[i + 1]
                  if target is not None:
                       lowercase_      :							str						=      (target >= l_idx) & (target < r_idx)
                       lowercase_      :							Dict						=      tf.where(_A     )
                       lowercase_      :							int						=      tf.boolean_mask(_A	,_A     ) - l_idx

                  if self.div_val == 1:
                       lowercase_      :							List[str]						=      self.out_layers[0][0][l_idx:r_idx]
                       lowercase_      :							Any						=      self.out_layers[0][1][l_idx:r_idx]
                  else:
                       lowercase_      :							Dict						=      self.out_layers[i][0]
                       lowercase_      :							Optional[int]						=      self.out_layers[i][1]

                  if i == 0:
                       lowercase_      :							List[Any]						=      tf.concat([cur_W, self.cluster_weight]	,0     )
                       lowercase_      :							List[str]						=      tf.concat([cur_b, self.cluster_bias]	,0     )

                       lowercase_      :							Tuple						=      self._logit(_A	,_A	,_A	,self.out_projs[0]     )
                       lowercase_      :							Union[str, Any]						=      tf.nn.log_softmax(_A     )
                       out.append(head_logprob[..., : self.cutoffs[0]]     )
                       if target is not None:
                            lowercase_      :							List[str]						=      tf.boolean_mask(_A	,_A     )
                            lowercase_      :							Tuple						=      self._gather_logprob(_A	,_A     )
                  else:
                       lowercase_      :							Any						=      self._logit(_A	,_A	,_A	,self.out_projs[i]     )
                       lowercase_      :							str						=      tf.nn.log_softmax(_A     )
                       lowercase_      :							Optional[int]						=      self.cutoffs[0] + i - 1  # No probability for the head cluster
                       lowercase_      :							int						=      head_logprob[..., cluster_prob_idx, None] + tail_logprob
                       out.append(_A     )
                       if target is not None:
                            lowercase_      :							List[Any]						=      tf.boolean_mask(_A	,_A     )
                            lowercase_      :							Tuple						=      tf.boolean_mask(_A	,_A     )
                            lowercase_      :							str						=      self._gather_logprob(_A	,_A     )
                            cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
                  if target is not None:
                       loss += tf.scatter_nd(_A	,-cur_logprob	,shape_list(_A     )     )
             lowercase_      :							str						=      tf.concat(_A	,axis=-1     )

        if target is not None:
             if return_mean:
                  lowercase_      :							int						=      tf.reduce_mean(_A     )
             # Add the training-time loss value to the layer using `self.add_loss()`.
             self.add_loss(_A     )

             # Log the loss as a metric (we could log arbitrary metrics,
             # including different metrics for training and inference.
             self.add_metric(_A	,name=self.name	,aggregation='mean' if return_mean else ''     )

        return out









'''simple docstring'''





from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCamelCase						:						Any  =					tuple[int, int]


class  snake_case__      :




					def __init__(  self  :		List[str]				, _A  :		set[int]				, _A  :		Mapping[EdgeT, int]      )    ->							None:
								UpperCAmelCase_      :       set[int]          =       vertices
								UpperCAmelCase_      :       dict[EdgeT, int]          =       {
								    (min(_A      ), max(_A      )): weight for edge, weight in edges.items()
								}




					def 		A      (  self  :		Union[str, Any]				, _A  :		EdgeT				, _A  :		int      )    ->							None:
								self.vertices.add(edge[0]      )
								self.vertices.add(edge[1]      )
								UpperCAmelCase_      :       List[str]          =       weight





					def 		A      (  self  :		str      )    ->							Graph:
								UpperCAmelCase_      :       Graph          =       Graph({min(self.vertices      )}				, {}      )
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int

								while len(subgraph.vertices      ) < len(self.vertices      ):
											UpperCAmelCase_      :       Optional[Any]          =       max(self.edges.values()      ) + 1
											for edge, weight in self.edges.items():
														if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																	if weight < min_weight:
																				UpperCAmelCase_      :       Dict          =       edge
																				UpperCAmelCase_      :       Tuple          =       weight

											subgraph.add_edge(_A				, _A      )

								return subgraph






def __UpperCAmelCase   (					A					:			str = "p107_network.txt"		)    ->   int:
			UpperCAmelCase_      :       str          =       os.path.abspath(os.path.dirname(A		)		)
			UpperCAmelCase_      :       str          =       os.path.join(A			,       A		)
			UpperCAmelCase_      :       dict[EdgeT, int]          =       {}
			UpperCAmelCase_      :       list[str]
			UpperCAmelCase_      :       int
			UpperCAmelCase_      :       int

			with open(A		) as f:
						UpperCAmelCase_      :       int          =       f.read().strip().split('''\n'''		)

			UpperCAmelCase_      :       Any          =       [line.split(''','''		) for line in data]

			for edgea in range(1			,       len(A		)		):
						for edgea in range(A		):
									if adjaceny_matrix[edgea][edgea] != "-":
												UpperCAmelCase_      :       Optional[int]          =       int(adjaceny_matrix[edgea][edgea]		)

			UpperCAmelCase_      :       Graph          =       Graph(set(range(len(A		)		)		)			,       A		)

			UpperCAmelCase_      :       Graph          =       graph.prims_algorithm()

			UpperCAmelCase_      :       int          =       sum(graph.edges.values()		)
			UpperCAmelCase_      :       int          =       sum(subgraph.edges.values()		)

			return initial_total - optimal_total


if __name__ == "__main__":
						print(f'''{solution() = }''')


